predictive modeling

Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care. For instance, CNN-based models excel in tasks like fracture detection and osteoarthritis grading, achieving high sensitivity and specificity. In surgical contexts, AI enhances procedures through robotic assistance and optimized preoperative planning, aiding in prosthetic sizing and minimizing complications. Additionally, predictive analytics during postoperative care enable tailored rehabilitation programs that improve recovery times. Despite these advancements, challenges such as data standardization and algorithm transparency hinder widespread adoption. Addressing these issues is crucial for maximizing AI's potential in orthopedic practice. This review emphasizes the synergistic relationship between AI and clinical expertise, highlighting opportunities to enhance diagnostics and streamline surgical procedures, ultimately driving patient-centric care.        Level of evidence: V

Prediction of Wegener's granulomatosis diagnosis and relapse is a complex process. In this study, we applied machine learning algorithms to predict Wegener's granulomatosis relapse.

In this research, 189 patients admitted to Amiralam Hospital were studied and followed for approximately 2 years. Patient features included demographics, organ involvement, symptoms, and other clinical data. Different popular machine learning algorithms were applied for predicting Wegener's granulomatosis relapse, including Support Vector Machines, Random Forest, Gradient Boosting, and XGBoost algorithms. The prediction model performance was measured for the different candidate prediction algorithms using accuracy, precision, recall, and F1-measure. The selected prediction model performance was calculated based on different relapse rates and major relapse occurrence according to Birmingham Vasculitis Activity Score (BVAS) fields.

Applying different machine learning algorithms, the XGBoost algorithm performed the best. The results indicated that the prediction model's performance increased when calculating higher relapse rate possibilities. The XGBoost model had 82% accuracy while predicting more than one relapse rate and 92% accuracy in predicting more than twice the relapse rate. We also calculated the SHAP value for the prediction model. The results indicated that Cr, BVAS, lymphocyte percentage, vitamin D, nose involvement, alkaline phosphatase, diagnosis age, white blood cell count, erythrocyte sedimentation rate, and initial nose presentation are the 10 most important features according to SHAP value.

In this study, we have developed Wegener's granulomatosis relapse prediction model using machine learning algorithms. We achieved reasonable precision and recall for early prediction and decisionmaking regarding Wegener's granulomatosis relapse.

Staphylococcus aureus is among the major causes of foodborne outbreaks globally. To limit its potential risks and predict its growth behaviors, it is crucial to define the growth boundaries of Staphylococcus aureus. So, this experiment was designed to estimate the growth behavior of Staphylococcus aureus in brain heart infusion (BHI) broth while affected by various concentrations of Carum copticum EO (0, 0.015, 0.030, 0.045%), pH (5, 6, 7), temperature (25, 35 ˚C), and inoculum levels (103, 105 CFU ml-1). The assay was performed with 48 treatment conditions in triplicate. Visible turbidity represents growth onset was checked daily during 30 days of trial. According to the accelerated failure time (AFT) approach, a parametric survival model was chosen to predict the impact of selected variables on Staphylococcus aureus growth. GC-MS assay had quantified sixteen (16) compounds constituting 98.88% of pure oil. Based on our findings, the major components of essential oil were identified as thymol (57.18%), ρ-cymene (22.55%), γ-terpinene (13.07%), and trans-anethole (1.7%). The MIC value of the EO was 0.625 μl ml-1. The median time to detection of bacterial growth was six days. All the predictor variables showed a significant effect on time to initiate the bacterial growth (p < 0.05). The ultimate model could precisely estimate the growth responses of Staphylococcus aureus.